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  • On September 11th, 2023,

  • weather predictions in the northeast

  • of the U.S. sounded like this.

  • All eyes are on Hurricane Lee.

  • The storm has strengthened back to a category 3.

  • We're expecting it to make

  • a northward turn over the next few days.

  • By September 16th, after being

  • downgraded, storm Lee made

  • landfall in Nova Scotia, Canada,

  • flooding roads, downing trees and

  • cutting out power for tens of

  • thousands of people along the East Coast.

  • At least five days before Hurricane Lee struck

  • land, weather forecasts had roughly

  • predicted its trajectory.

  • But another forecast beat them

  • three days before weather stations,

  • an AI model created by Google

  • predicted the cyclone's path.

  • The AI revolution has reached

  • meteorology, and it's at a

  • time when we are responding to

  • extreme weather more than ever.

  • We're about to find out if it will

  • help us prepare by bringing the

  • future into clear view.

  • Predicting future weather more than

  • a few hours out starts with creating

  • a snapshot of Earth's current

  • atmosphere.

  • Scientists do that by collecting

  • data from sources like satellites

  • and weather stations and buoys

  • located around the world, taking

  • pictures of clouds and measuring

  • temperature and pressure and wind

  • speed and humidity.

  • All that disparate data gets

  • fed into computers, which generate

  • a 3D grid of boxes that represent

  • the atmosphere, both vertically and

  • horizontally.

  • Computers then do a lot of physics

  • to determine how these conditions

  • interact with each other, and they

  • produce a forecast.

  • I think any forecast has like

  • 150 trillion calculations.

  • It's pretty amazing.

  • All that math requires some of the

  • world's most powerful

  • supercomputers.

  • The two big ones are run by the

  • European Centre for Medium-Range

  • Weather Forecasts and the National

  • Weather Service in the U.S.

  • To make a local weather forecast

  • from this global model,

  • meteorologists zoom in and refine

  • their own forecasts with their local

  • expertise.

  • Like if they live in a hilly area

  • or a flat area or near a lake,

  • they'll adjust those models and do

  • their own professional

  • interpretation based on their

  • area.

  • No matter what, this initial 3D grid

  • of that sphere is never going to

  • exactly replicate reality.

  • There's too many gaps in the data we

  • can measure.

  • That means forecasts get blurrier

  • the further out you go.

  • Which is why the big weather centers

  • don't just generate one forecast.

  • They tweak the initial data and

  • produce up to 50 forecasts.

  • It's called ensemble forecasting,

  • and it helps meteorologists measure

  • uncertainty.

  • If all 50 forecast looks similar,

  • there's a higher certainty in the

  • prediction. But if there's a lot of

  • variation there's much less.

  • We got to kind of keep an eye to the

  • sky. There's a potential of another

  • storm in the works.

  • This went from 0% chance to

  • 40% at two o'clock,

  • 70% at 8:00,

  • and at 11:00.

  • We have a tropical storm.

  • Weather centers only release their

  • forecasts every six hours.

  • Because that's all today's computing

  • power will allow.

  • But what if that limit didn't exist?

  • Before we explain that, we'll hear

  • from the sponsor of this video.

  • This episode is presented by

  • Microsoft Copilot for Microsoft

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  • AI. Microsoft does not influence

  • the editorial process of our videos,

  • but they do help make videos like

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  • To learn more, you can go to

  • microsoft.com/copilotforwork.

  • Now back to our video.

  • In 2020, a group of researchers

  • published a data set called Era5

  • five, which contained about 40 years

  • of the Earth's hourly weather data.

  • The data set was just primed for

  • using AI because it was huge.

  • The data is nice and smooth,

  • there's no missing values, and

  • it's free just to step in.

  • And say, "Hey, you have terabytes of data?

  • I can learn how weather moves."

  • AI models learned how weather moves,

  • not through applying trillions of

  • physics equations to the Earth's

  • atmosphere, but by being trained

  • on Era5's enormous historical

  • data set.

  • Researchers gave the models a

  • snapshot of weather conditions,

  • ask them to make a prediction, and

  • then scored them on how closely that

  • prediction matched what really

  • happened.

  • After a while, the models eventually

  • got really good at this.

  • By 2023, the tech companies

  • Google, Huawei and Nvidia

  • had developed models that rivaled

  • traditional forecasting on variables

  • like surface temperature, humidity

  • and wind speed, and on some extreme

  • weather events like the paths of

  • tropical cyclones, atmospheric

  • rivers, and extreme temperatures.

  • These AI models still rely on the

  • same observation data from the big

  • weather centers, the data that

  • creates that initial 3D grid

  • snapshot.

  • But they don't require anything

  • close to six hours to produce a

  • prediction. Huawei's PanguWeather

  • model, for example, can produce a

  • week long forecast in 1.4 seconds.

  • Which means that we spent over a century

  • figuring out the physics, the

  • atmospheric science and the

  • computational skill to bring us our

  • modern day weather forecast.

  • And now suddenly we have these AI

  • models that have come out of, you

  • know, the past 2 or 3 years,

  • and they're getting the same skill,

  • and now they run on a

  • modest laptop.

  • Despite the impressive results from

  • these first AI models, there's still

  • lots of work to do.

  • Google's GraphCast predicted

  • Hurricane Lee's path faster than

  • traditional models, but it didn't

  • prove it could predict a hurricane's

  • intensity, which is a trickier

  • calculation to make.

  • These AI models are incentivized to

  • get as many correct answers as they

  • can through the scoring system.

  • If you swing for it, swing for the

  • fences, right?

  • If it misses, the model is

  • penalized very large. It says "No, you

  • should never do that.

  • Don't swing for the fences because

  • the error is going to be huge."

  • But by prioritizing safer,

  • correct answers to boost the model's

  • score, it could miss rare,

  • outlier weather events.

  • Plus, they are learning from 40

  • years of history, and historical

  • weather has fewer extreme events

  • than we do today or will have in

  • the future due to climate change.

  • But a big reason for optimism with

  • these AI models comes from their

  • ensemble forecasting.

  • Instead of the traditional 50

  • ensemble forecasts, they can predict

  • a thousand or more because they're

  • freed from computing and time

  • constraints.

  • There's always going to be

  • uncertainty in a weather prediction,

  • but larger ensembles will help us

  • measure that uncertainty better.

  • That's an extremely useful context

  • to say you are a emergency manager

  • down in Florida who's dealing with

  • the very difficult decision:

  • Are you going to, you know, order an

  • evacuation or not?

  • You want as much information about

  • the uncertainty as possible.

  • Large ensembles might also catch

  • a rare weather event that a 50-member ensemble would miss.

  • Or, measure the probability of

  • weather events even further into the

  • future than our 10 day forecast.

  • Are we magically going to get a

  • crystal ball that lets us foresee

  • perfectly into the future?

  • Probably not.

  • I think especially on like sub

  • seasonal timescales, like multiple

  • months out, we're going to be able

  • to frame the statistical question

  • with a lot more specificity

  • and probably a much better

  • quantification of the uncertainty.

  • We do have a new winter storm

  • warning.

  • One thing we shouldn't

  • expect to change any time soon is

  • the role of the meteorologist...

  • at least the ones you see on TV.

  • If only because we fundamentally

  • have to communicate uncertainty, and

  • we have to walk through all the

  • various what-ifs.

  • And a human is the best tool that we

  • have today to effectively

  • communicate that and help somebody

  • else make a decision.

  • AI forecasting models are still in

  • an experimental phase, but the

  • European Centre for Medium-Range

  • Weather Forecasts has started

  • publishing AI forecast alongside

  • their traditional ones for the

  • public to compare.

  • When we check the weather in the

  • very near future, it might be

  • powered by AI instead of physics

  • based models or a combination

  • of the two.

  • And if we get things right, we'll

  • have a sharper view of the weather

  • events that we need to prepare for

  • the most.

On September 11th, 2023,

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Can AI help us predict extreme weather?(Can AI help us predict extreme weather?)

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    林宜悉 に公開 2024 年 02 月 24 日
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